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docs/_build/html/_sources/guides/data/data_prepare.rst.txt

@@ -8,11 +8,6 @@
 Data Preparation
 ======================================
 
-In this section, I will introduce the data preparation module of PiML. In data preparing function, you are allowed
-to set the detailed config of the model training procedure, including task configs like target variable, task type, and sample_weight.
-And there is the data split setting such as split method, split ratio, and random seed in the data preparing setting to decide how to split data for the experiment.
-Besides the setting, you can also get train test data distance results.
-
 This section introduces the data preparation module of PiML. In this function, you can set the detailed configuration of the model training procedure, including task configurations like target variable, task type, and sample_weight. Further, there is the data split setting such as split method, split ratio, and random seed to decide how to split data for the experiment. Besides the setting, you can also get results on the distances between train test datasets.
 
 

docs/_build/html/_sources/guides/data/data_summary.rst.txt

@@ -7,8 +7,6 @@
 ==========================
 Data Summary
 ==========================
-This functionality summarizes basic data statistics and sets the meta information for the features. In this function, you can get the summary information of data based on its data type and also change the feature type and remove features.
-
 
 Data summary is the process to summarize basic data statistics and setting the meta info of features.
 In this function, you could not only get the summary information of data based on its data type, but also you can change the feature type and remove features.

docs/_build/html/guides/data/data_prepare.html

@@ -248,10 +248,6 @@
 
 </style><section id="data-preparation">
 <h1><span class="section-number">2.3. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading">¶</a></h1>
-<p>In this section, I will introduce the data preparation module of PiML. In data preparing function, you are allowed
-to set the detailed config of the model training procedure, including task configs like target variable, task type, and sample_weight.
-And there is the data split setting such as split method, split ratio, and random seed in the data preparing setting to decide how to split data for the experiment.
-Besides the setting, you can also get train test data distance results.</p>
 <p>This section introduces the data preparation module of PiML. In this function, you can set the detailed configuration of the model training procedure, including task configurations like target variable, task type, and sample_weight. Further, there is the data split setting such as split method, split ratio, and random seed to decide how to split data for the experiment. Besides the setting, you can also get results on the distances between train test datasets.</p>
 <section id="setting">
 <h2><span class="section-number">2.3.1. </span>Setting<a class="headerlink" href="#setting" title="Permalink to this heading">¶</a></h2>

docs/_build/html/guides/data/data_summary.html

@@ -246,7 +246,6 @@
 
 </style><section id="data-summary">
 <h1><span class="section-number">2.2. </span>Data Summary<a class="headerlink" href="#data-summary" title="Permalink to this heading">¶</a></h1>
-<p>This functionality summarizes basic data statistics and sets the meta information for the features. In this function, you can get the summary information of data based on its data type and also change the feature type and remove features.</p>
 <p>Data summary is the process to summarize basic data statistics and setting the meta info of features.
 In this function, you could not only get the summary information of data based on its data type, but also you can change the feature type and remove features.</p>
 <section id="summary-statistics">